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Buying question

Recommend an observability tool for debugging retrieval quality, context relevance, groundedness, and hallucinations in RAG systems.

Which brands each model API named when asked this question, and which pages it cited. Answer text is not published.
Snapshot Oct 22, 2026. Each category is re-run in rotation, every few weeks.

Recommendation

Kind of question

3

Model APIs

3

Brands named

6

Pages cited

Brands

Brands the answers named

Rank#BrandModel APIsMentionsOwn-site citationsNamed by
1Arize AI / Phoenixarize.com234OpenAI · Google
2Galileogalileo.ai111Anthropic
3Braintrustbraintrust.dev110Anthropic

Model APIs

What each model API returned

Model APIModelBrands namedCitationsDomainsAnswer hash
OpenAIopenai/gpt-6-lunaArize AI / Phoenix41630e9ced…be205
Anthropicanthropic/claude-haiku-5.5Braintrust · Galileo225856a243…315a2
Googlegoogle/gemini-3.1-flash-liteArize AI / Phoenix00e20c0065…21ef8

Sources

Pages the answers cited

Snapshots
b508948a-6a42-4345-b118-2d4a4bb38b87
Question set
llm-observability-platforms-2026-07-27
Benchmark version
4-llm-observability-platforms-2026-07-27-models-16124f05e021
What is kept
Measured in AI observability. Full answers are not stored or published, and no customer data is included.

Enter your website. In about two minutes, Rank.ai asks ChatGPT, Claude and Gemini 12 questions your customers ask and grades how often they name you.

  • Your grade out of 100How often AI names you, cites your site, and how high it ranks you.
  • Who gets namedEvery competitor in the answers, most named first.
  • The pages AI readsThe sources behind each answer.
  • Three fixesWhat to fix first, with a brief for the first article.